Sort by
Refine Your Search
-
understanding of physiology, MATLAB and statistic skills, and advanced technical understanding Proactive and inquisitive personality, eager to exploring new approaches, self-motivated and team-oriented Excellent
-
degree in Computer Science, Data Science, or a related technical field with strong foundations in machine learning OR Master's degree in Mathematics, Statistics, Physics, or other quantitative discipline
-
-measurement techniques and/or molecular analyses - Strong interest in agroecological processes and in relevant experimental studies - Experience with statistics and/or bioinformatics and programming skills in R
-
spectroscopy, along with cutting-edge machine learning approaches and statistical analysis, to enable timely identification of high risk for the chronic wound formation. Your profile We are looking for a highly
-
description In particular, this PhD research project examines: The socio-spatial vulnerability to hard densification of population groups living in older housing stock through statistical socio-spatial analysis
-
will be directly evaluated in industrial settings, establishing a feedback loop between lab scale observations and statistical relevance in production environments. PhD student position on advanced
-
Administration, or a related field. Independence and high motivation for academic research in the group’s topics. Proficiency in empirical research methods, including statistics and data analysis, and the
-
Management, Business Administration, or a related field. Independence and high motivation for academic research in the group’s topics. Proficiency in empirical research methods, including statistics and data
-
with online newspapers and use social media data. Our research employs advanced statistical methods for causal inference and we develop state-of-the-art machine learning classifiers for both harmful and high
-
discourse and reduce hate speech. We work together with online newspapers and use social media data. Our research employs advanced statistical methods for causal inference and we develop state-of-the-art